Download or read book Pattern Discovery in Bioinformatics written by Laxmi Parida and published by CRC Press. This book was released on 2007-07-04 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systema
Download or read book Biological Pattern Discovery With R Machine Learning Approaches written by Zheng Rong Yang and published by World Scientific. This book was released on 2021-09-17 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.
Download or read book Discriminative Pattern Discovery on Biological Networks written by Fabio Fassetti and published by Springer. This book was released on 2017-09-01 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes). Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples). In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.
Download or read book Pattern Discovery in Biomolecular Data written by Jason T. L. Wang and published by Oxford University Press. This book was released on 1999-10-28 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.
Download or read book Advances in Genomic Sequence Analysis and Pattern Discovery written by Laura Elnitski and published by World Scientific. This book was released on 2011 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mapping the genomic landscapes is one of the most exciting frontiers of science. We have the opportunity to reverse engineer the blueprints and the control systems of living organisms. Computational tools are key enablers in the deciphering process. This book provides an in-depth presentation of some of the important computational biology approaches to genomic sequence analysis. The first section of the book discusses methods for discovering patterns in DNA and RNA. This is followed by the second section that reflects on methods in various ways, including performance, usage and paradigms.
Download or read book Pattern Discovery Using Sequence Data Mining written by Pradeep Kumar and published by . This book was released on 2011-07-01 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"--
Download or read book Pattern Discovery in Biology and Strings Sorting written by Ezekiel Adebiyi and published by . This book was released on 2002 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Pattern Recognition in Computational Molecular Biology written by Mourad Elloumi and published by John Wiley & Sons. This book was released on 2015-11-30 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.
Download or read book Computational Intelligence and Pattern Analysis in Biology Informatics written by Ujjwal Maulik and published by John Wiley & Sons. This book was released on 2011-03-21 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers Chapters authored by leading researchers in CI in biology informatics. Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. Supplementary material included: program code and relevant data sets correspond to chapters.
Download or read book Biological Pattern Discovery with R written by Yang Rong Zheng and published by . This book was released on 2021 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neuromorphic Olfaction written by Krishna C. Persaud and published by CRC Press. This book was released on 2016-04-19 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many advances have been made in the last decade in the understanding of the computational principles underlying olfactory system functioning. Neuromorphic Olfaction is a collaboration among European researchers who, through NEUROCHEM (Fp7-Grant Agreement Number 216916)-a challenging and innovative European-funded project-introduce novel computing p
Download or read book Advances in Computational Biology written by Hamid R. Arabnia and published by Springer Science & Business Media. This book was released on 2010-09-24 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of The 2009 International Conference on Bioinformatics and Computational Biology in Las Vegas, NV, July 13-16, 2009. Recent advances in Computational Biology are covered through a variety of topics. Both inward research (core areas of computational biology and computer science) and outward research (multi-disciplinary, Inter-disciplinary, and applications) will be covered during the conferences. These include: Gene regulation, Gene expression databases, Gene pattern discovery and identification, Genetic network modeling and inference, Gene expression analysis, RNA and DNA structure and sequencing, Biomedical engineering, Microarrays, Molecular sequence and structure databases, Molecular dynamics and simulation, Molecular sequence classification, alignment and assembly, Image processing In medicine and biological sciences, Sequence analysis and alignment, Informatics and Statistics in Biopharmaceutical Research, Software tools for computational biology and bioinformatics, Comparative genomics; and more.
Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Download or read book Unsupervised Feature Extraction Applied to Bioinformatics written by Y-h. Taguchi and published by Springer Nature. This book was released on 2019-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.
Download or read book Pattern Recognition in Bioinformatics written by Tjeerd M.H. Dijkstra and published by Springer Science & Business Media. This book was released on 2010-09-20 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Pattern Recognition in Bioinformatics, PRIB 2010, held in Nijmegen, The Netherlands, in September 2010. The 38 revised full papers presented were carefully reviewed and selected from 46 submissions. The field of bioinformatics has two main objectives: the creation and maintenance of biological databases and the analysis of life sciences data in order to unravel the mysteries of biological function. Computer science methods such as pattern recognition, machine learning, and data mining have a great deal to offer the field of bioinformatics.
Download or read book Biodata Mining and Visualization Novel Approaches written by and published by World Scientific. This book was released on 2009 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: "There is a lack of an exposition on interdisciplinary and innovative methods of data mining and visualization for biodata. This book fills the gap by introducing an interdisciplinary set of the most recent methods and references on novel techniques from artificial intelligence, data mining, engineering, pattern recognition, and ontological data mining fields that are applicable to bioinformatics. The latest novel approaches are explained in detail, their advantages and disadvantages are summarized, and pointers to the future development of new applications are given. By widening the pool from which biologists and bioinformaticians can adopt methods for biodata mining and visualization, computational data mining experts in nonbiological fields are also encouraged to utilize their expertise in order to contribute to the progress of computational biology, thus enhancing the collaboration between these two disciplines."--Publisher's website
Download or read book Data Mining for Bioinformatics written by Sumeet Dua and published by CRC Press. This book was released on 2012-11-06 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field. The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections: Supplies a complete overview of the evolution of the field and its intersection with computational learning Describes the role of data mining in analyzing large biological databases—explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data Covers supervised learning using classification techniques most commonly used in bioinformatics—addressing the need for validation and benchmarking of inferences derived using either clustering or classification The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biological databases, it considers systems of both single and ensemble classifiers and shares effort-saving tips for model selection and performance estimation strategies.
